Exploiting spatial and temporal coherence in GPU-based volume rendering

Effizienz spielt eine wichtige Rolle bei der Darstellung von Volumendaten, selbst wenn leistungsstarke Grafikhardware zur Verfügung steht, da steigende Datensatzgrößen und höhere Anforderungen an Visualisierungstechniken Fortschritte bei Grafikprozessoren ausgleichen. In dieser Dissertation wird untersucht, wie räumliche und zeitliche Kohärenz in Volumendaten zur Optimierung von Volumenrendering genutzt werden kann. Es werden mehrere neue Ansätze für statische und zeitvariante Daten eingeführt, die verschieden Arten von Kohärenz in verschiedenen Stufen der Volumenrendering-Pipeline ausnutzen. Zu den vorgestellten Beschleunigungstechniken gehört Empty Space Skipping mittels Occlusion Frustums, eine auf Slabs basierende Cachestruktur für Raycasting und ein verlustfreies Kompressionsscheme für zeitvariante Daten. Die Algorithmen wurden zur Verwendung mit GPU-basiertem Volumen-Raycasting entworfen und nutzen die Fähigkeiten moderner Grafikprozessoren, insbesondere Stream Processing. Efficiency is a key aspect in volume rendering, even if powerful graphics hardware is employed, since increasing data set sizes and growing demands on visualization techniques outweigh improvements in graphics processor performance. This dissertation examines how spatial and temporal coherence in volume data can be used to optimize volume rendering. Several new approaches for static as well as for time-varying data sets are introduced, which exploit different types of coherence in different stages of the volume rendering pipeline. The presented acceleration algorithms include empty space skipping using occlusion frustums, a slab-based cache structure for raycasting, and a lossless compression scheme for time-varying data. The algorithms were designed for use with GPU-based volume raycasting and to efficiently exploit the features of modern graphics processors, especially stream processing

Towards Predictive Rendering in Virtual Reality

The strive for generating predictive images, i.e., images representing radiometrically correct renditions of reality, has been a longstanding problem in computer graphics. The exactness of such images is extremely important for Virtual Reality applications like Virtual Prototyping, where users need to make decisions impacting large investments based on the simulated images. Unfortunately, generation of predictive imagery is still an unsolved problem due to manifold reasons, especially if real-time restrictions apply. First, existing scenes used for rendering are not modeled accurately enough to create predictive images. Second, even with huge computational efforts existing rendering algorithms are not able to produce radiometrically correct images. Third, current display devices need to convert rendered images into some low-dimensional color space, which prohibits display of radiometrically correct images. Overcoming these limitations is the focus of current state-of-the-art research. This thesis also contributes to this task. First, it briefly introduces the necessary background and identifies the steps required for real-time predictive image generation. Then, existing techniques targeting these steps are presented and their limitations are pointed out. To solve some of the remaining problems, novel techniques are proposed. They cover various steps in the predictive image generation process, ranging from accurate scene modeling over efficient data representation to high-quality, real-time rendering. A special focus of this thesis lays on real-time generation of predictive images using bidirectional texture functions (BTFs), i.e., very accurate representations for spatially varying surface materials. The techniques proposed by this thesis enable efficient handling of BTFs by compressing the huge amount of data contained in this material representation, applying them to geometric surfaces using texture and BTF synthesis techniques, and rendering BTF covered objects in real-time. Further approaches proposed in this thesis target inclusion of real-time global illumination effects or more efficient rendering using novel level-of-detail representations for geometric objects. Finally, this thesis assesses the rendering quality achievable with BTF materials, indicating a significant increase in realism but also confirming the remainder of problems to be solved to achieve truly predictive image generation

Adaptive remote visualization system with optimized network performance for large scale scientific data

This dissertation discusses algorithmic and implementation aspects of an automatically configurable remote visualization system, which optimally decomposes and adaptively maps the visualization pipeline to a wide-area network. The first node typically serves as a data server that generates or stores raw data sets and a remote client resides on the last node equipped with a display device ranging from a personal desktop to a powerwall. Intermediate nodes can be located anywhere on the network and often include workstations, clusters, or custom rendering engines. We employ a regression model-based network daemon to estimate the effective bandwidth and minimal delay of a transport path using active traffic measurement. Data processing time is predicted for various visualization algorithms using block partition and statistical technique. Based on the link measurements, node characteristics, and module properties, we strategically organize visualization pipeline modules such as filtering, geometry generation, rendering, and display into groups, and dynamically assign them to appropriate network nodes to achieve minimal total delay for post-processing or maximal frame rate for streaming applications. We propose polynomial-time algorithms using the dynamic programming method to compute the optimal solutions for the problems of pipeline decomposition and network mapping under different constraints. A parallel based remote visualization system, which comprises a logical group of autonomous nodes that cooperate to enable sharing, selection, and aggregation of various types of resources distributed over a network, is implemented and deployed at geographically distributed nodes for experimental testing. Our system is capable of handling a complete spectrum of remote visualization tasks expertly including post processing, computational steering and wireless sensor network monitoring. Visualization functionalities such as isosurface, ray casting, streamline, linear integral convolution (LIC) are supported in our system. The proposed decomposition and mapping scheme is generic and can be applied to other network-oriented computation applications whose computing components form a linear arrangement

Interactive volume ray tracing

Die Visualisierung von volumetrischen Daten ist eine der interessantesten, aber sicherlich auch schwierigsten Anwendungsgebiete innerhalb der wissenschaftlichen Visualisierung. Im Gegensatz zu Oberflächenmodellen, repräsentieren solche Daten ein semi-transparentes Medium in einem 3D-Feld. Anwendungen reichen von medizinischen Untersuchungen, Simulation physikalischer Prozesse bis hin zur visuellen Kunst. Viele dieser Anwendungen verlangen Interaktivität hinsichtlich Darstellungs- und Visualisierungsparameter. Der Ray-Tracing- (Stahlverfolgungs-) Algorithmus wurde dabei, obwohl er inhärent die Interaktion mit einem solchen Medium simulieren kann, immer als zu langsam angesehen. Die meisten Forscher konzentrierten sich vielmehr auf Rasterisierungsansätze, da diese besser für Grafikkarten geeignet sind. Dabei leiden diese Ansätze entweder unter einer ungenügenden Qualität respektive Flexibilität. Die andere Alternative besteht darin, den Ray-Tracing-Algorithmus so zu beschleunigen, dass er sinnvoll für Visualisierungsanwendungen benutzt werden kann. Seit der Verfügbarkeit moderner Grafikkarten hat die Forschung auf diesem Gebiet nachgelassen, obwohl selbst moderne GPUs immer noch Limitierungen, wie beispielsweise der begrenzte Grafikkartenspeicher oder das umständliche Programmiermodell, enthalten. Die beiden in dieser Arbeit vorgestellten Methoden sind deshalb vollständig softwarebasiert, da es sinnvoller erscheint, möglichst viele Optimierungen in Software zu realisieren, bevor eine Portierung auf Hardware erfolgt. Die erste Methode wird impliziter Kd-Baum genannt, eine hierarchische und räumliche Beschleunigungstruktur, die ursprünglich für die Generierung von Isoflächen reguläre Gitterdatensätze entwickelt wurde. In der Zwischenzeit unterstützt sie auch die semi-transparente Darstellung, die Darstellung von zeitabhängigen Datensätzen und wurde erfolgreich für andere Anwendungen eingesetzt. Der zweite Algorithmus benutzt so genannte Plücker-Koordinaten, welche die Implementierung eines schnellen inkrementellen Traversierers für Datensätze erlauben, deren Primitive Tetraeder beziehungsweise Hexaeder sind. Beide Algorithmen wurden wesentlich optimiert, um eine interaktive Bildgenerierung volumetrischer Daten zu ermöglichen und stellen deshalb einen wichtigen Beitrag hin zu einem flexiblen und interaktiven Volumen-Ray-Tracing-System dar.Volume rendering is one of the most demanding and interesting topics among scientific visualization. Applications include medical examinations, simulation of physical processes, and visual art. Most of these applications demand interactivity with respect to the viewing and visualization parameters. The ray tracing algorithm, although inherently simulating light interaction with participating media, was always considered too slow. Instead, most researchers followed object-order algorithms better suited for graphics adapters, although such approaches often suffer either from low quality or lack of flexibility. Another alternative is to speed up the ray tracing algorithm to make it competitive for volumetric visualization tasks. Since the advent of modern graphic adapters, research in this area had somehow ceased, although some limitations of GPUs, e.g. limited graphics board memory and tedious programming model, are still a problem. The two methods discussed in this thesis are therefore purely software-based since it is believed that software implementations allow for a far better optimization process before porting algorithms to hardware. The first method is called implicit kd-tree, which is a hierarchical spatial acceleration structure originally developed for iso-surface rendering of regular data sets that now supports semi-transparent rendering, time-dependent data visualization, and is even used in non volume-rendering applications. The second algorithm uses so-called Plücker coordinates, providing a fast incremental traversal for data sets consisting of tetrahedral or hexahedral primitives. Both algorithms are highly optimized to support interactive rendering of volumetric data sets and are therefore major contributions towards a flexible and interactive volume ray tracing framework

Exploring manycore architectures for next-generation HPC systems through the MANGO approach

[EN] The Horizon 2020 MANGO project aims at exploring deeply heterogeneous accelerators for use in High-Performance Computing systems running multiple applications with different Quality of Service (QoS) levels. The main goal of the project is to exploit customization to adapt computing resources to reach the desired QoS. For this purpose, it explores different but interrelated mechanisms across the architecture and system software. In particular, in this paper we focus on the runtime resource management, the thermal management, and support provided for parallel programming, as well as introducing three applications on which the project foreground will be validated.This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 671668.Flich Cardo, J.; Agosta, G.; Ampletzer, P.; Atienza-Alonso, D.; Brandolese, C.; Cappe, E.; Cilardo, A.... (2018). Exploring manycore architectures for next-generation HPC systems through the MANGO approach. Microprocessors and Microsystems. 61:154-170. https://doi.org/10.1016/j.micpro.2018.05.011S1541706

Efficient rendering of large 3-D and 4-D scalar fields

Rendering volumetric data, as a compute/communication intensive and highly parallel application, represents the characteristics of future workloads for desktop computers. Interactively rendering volumetric data has been a challenging problem due to its high computational and communication requirements. With the consistent trend toward high resolution data, it has remained a difficult problem despite the continuous increase in processing power, because of the increasing performance gap between computation and communication. On the other hand, the new multi-core architecture trend in computational units in PC, which can be characterized by parallelism and heterogeneity, provides both opportunities and challenges. While the new on-chip parallel architectures offer opportunities for extremely high performance, widespread use of those parallel processors requires extensive changes in previous algorithms to take advantage of the new architectures. In this dissertation, we develop new methods and techniques to support interactive rendering of large volumetric data. In particular, we present a novel method to layout data on disk for efficiently performing an out-of-core axis-aligned slicing of large multidimensional scalar fields. We also present a new method to efficiently build an out-of-core indexing structure for n-dimensional volumetric data. Then, we describe a streaming model for efficiently implementing volume ray casting on a heterogeneous compute resource environment. We describe how we implement the model on SONY/TOSHIBA/IBM Cell Broadband Engine and on NVIDIA CUDA architecture. Our results show that our out-of-core techniques significantly reduce the communication bandwidth requirements and that our streaming model very effectively makes use of the strengths of those heterogeneous parallel compute resource environment for volume rendering. In all cases, we achieve scalability and load balancing, while hiding memory latency

Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning

Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and direct surgical procedures, and to track the development of bone-related diseases. This often involves radiologists who have to annotate bones manually or in a semi-automatic way, which is a time consuming task. Their annotation workload can be reduced by automated segmentation and detection of individual bones. This automation of distinct bone segmentation not only has the potential to accelerate current workflows but also opens up new possibilities for processing and presenting medical data for planning, navigation, and education. In this thesis, we explored the use of deep learning for automating the segmentation of all individual bones within an upper-body CT scan. To do so, we had to find a network architec- ture that provides a good trade-off between the problem’s high computational demands and the results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out to eliminate the most prevalent types of error. To do so, we introduced an novel method called binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin- guishing bone from non-bone is conducted separately from identifying the individual bones. Both predictions are then merged, which leads to superior results. Another type of error is tack- led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input into the network while keeping the growth of additional pixels in check. Overall, we present a deep-learning-based method that reliably segments most of the over one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter quickly enough to be used in interactive software. Our algorithm has been included in our groups virtual reality medical image visualisation software SpectoVR with the plan to be used as one of the puzzle piece in surgical planning and navigation, as well as in the education of future doctors

Visual-auditory visualisation of dynamic multi-scale heterogeneous objects.

The multi-scale phenomena analysis is an area of active research that is connecting simulations with experiments to get a correct insight into the compound dynamic structure. Visualisation is a challenging task due to a large amount of data and a wide range of complex data representations. The analysis of dynamic multi-scale phenomena requires a combination of geometric modelling and rendering techniques for the analysis of the changes in the internal structure in the case of data coming from different sources of various nature. Moreover, the area often addresses the limitations of solely visual data representation and considers the introduction of other sensory stimuli as a well-known tool to enhance visual analysis. However, there is a lack of software tools allowing perform an advanced real-time analysis of heterogeneous phenomena properties. The hardware-accelerated volume rendering allows getting insight into the internal structure of complex multi-scale phenomena. The technique is convenient for detailed visual analysis and highlights the features of interest in complex structures and is an area of active research. However, the conventional volume visualisation is limited to the use of transfer functions that operate on homogeneous material and, as a result, does not provide flexibility in geometry and material distribution modelling that is crucial for the analysis of heterogeneous objects. Moreover, the extension to visual-auditory analysis emphasises the necessity to review the entire conventional volume visualisation pipeline. The multi-sensory feedback highly depends on the use of modern hardware and software advances for real-time modelling and evaluation. In this work, we explore the aspects of the design of visual-auditory pipelines for the analysis of dynamic multi-scale properties of heterogeneous objects that can allow overcoming well-known problems of complex representations solely visual analysis. We consider the similarities between light and sound propagation as a solution to the problem. The approach benefits from a combination of GPU accelerated ray-casting, geometry, optical and auditory properties modelling. We discuss how the modern GPU techniques application in those areas allows introducing a unified approach to the visual-auditory analysis of dynamic multi-scale heterogeneous objects. Similarly to the conventional volume rendering technique based on light propagation, we model auditory feedback as a result of initial impulse propagation through 3D space and its digital representation as a sampled sound wave obtained with the ray-casting procedure. The auditory stimuli can complement visual ones in the analysis of the dynamic multi-scale heterogeneous object. We propose a framework that facilitates the design of dynamic multi-scale heterogeneous objects visual-auditory pipeline and discuss the framework application for two case studies. The first is a molecular phenomena study that is a result of molecular dynamics simulation and quantum simulation. The second explores microstructures in digital fabrication with an arbitrary irregular lattice structure. For considered case studies, the visual-auditory techniques facilitate the interactive analysis of both spatial structure and internal multi-scale properties of volume nature in complex heterogeneous objects. A GPU-accelerated framework for visual-auditory analysis of heterogeneous objects can be applied and extend beyond this research. Thus, to specify the main direction of such extension from the point of view of the potential users, strengthen the value of this research as well as to evaluate the vision of the application of the techniques described above, we carry out a preliminary evaluation. The user study aims to compare our expectations on the visual-auditory approach with the views of the potential users of this system if it is implemented as a software product. A preliminary evaluation study was carried out with limitations imposed by 2020/2021 restrictions. However, it confirms that the main direction for the visual-auditory analysis of heterogeneous objects has been identified correctly and visual and auditory stimuli can complement each other in the analysis of both volume and spatial distribution properties of heterogeneous phenomena. The user reviews also highlight the necessary enhancements that should be introduced to the approach in terms of the design of more complex user interfaces and consideration of additional application cases. To provide a more detailed picture on evaluation results and recommendations introduced, we also identify the key factors that define the user vision of the approach further enhancement and its possible application areas, such as users experience in the area of complex physical phenomena analysis or multi-sensory area. The discussed in this work aspects of heterogeneous objects analysis task, theoretical and practical solutions allow considering the application, further development and enhancement of the results in multidisciplinary areas of GPU accelerated High-performance visualisation pipelines design and multi-sensory analysis

Rapid development of applications for the interactive visual analysis of multimodal medical data

Multimodale medizinische Volumendaten gewinnen zunehmend an Verbreitung. Wir diskutieren verschiedene interaktive Applikationen welche den Nutzer bei der Analyse dieser Daten unterstützen. Alle Applikationen basieren auf Erweiterungen des Voreen Frameworks, welche ebenfalls in dieser Dissertation diskutiert werden. With multimodal volumetric medical data sets becoming more common due to the increasing availability of scanning hardware, software for the visualization and analysis of such data sets needs to become more efficient as well in order to prevent overloading the user with data. This dissertation presents several interactive techniques for the visual analysis of medical volume data. All applications are based on extensions to the Voreen volume rendering framework, which we will discuss first. Since visual analysis applications are interactive by definition, we propose a general-purpose navigation technique for volume data. Next, we discuss our concepts for the interactive planning of brain tumor resections. Finally, we present two systems designed to work with images of vasculature. First, we discuss an interactive vessel segmentation system enabling an efficient, visually supported workflow. Second, we propose an application for the visual analysis of PET tracer uptake along vessels